Client Report - [Insert Project Title]

On this page

• Elevator pitch
• QUESTION|TASK 1
• QUESTION|TASK 2
• QUESTION|TASK 3
• QUESTION|TASK 4
• QUESTION|TASK 5
• STRETCH QUESTION|TASK 1

import pandas as pd
import numpy as np
from lets_plot import *

LetsPlot.setup_html(isolated_frame=True)

# Learn morea about Code Cells: https://quarto.org/docs/reference/cells/cells-jupyter.html

# Include and execute your code here
df = pd.read_json("https://github.com/byuidatascience/data4missing/raw/master/data-raw/flights_missing/flights_missing.json")

Elevator pitch

A SHORT (2-3 SENTENCES) PARAGRAPH THAT DESCRIBES KEY INSIGHTS TAKEN FROM METRICS IN THE PROJECT RESULTS THINK TOP OR MOST IMPORTANT RESULTS. (Note: this is not a summary of the project, but a summary of the results.)

The following graphs show that the best month to fly is december to avoid delays is september. The most common type of delay you are likley to encounter is weather delays. The airport out of the list with the lowest average delay is SAN with a delay of .79 hours. The worst is Orlando with an average delay of 1.13 hours.

QUESTION|TASK 1

Fix all of the varied missing data types in the data to be consistent (all missing values should be displayed as “NaN”). In your report include one record example (one row) from your new data, in the raw JSON format. Your example should display the “NaN” for at least one missing value.__

type your results and analysis here

Dropped any outliers or missing data replacing them with the value NAN

df.num_of_delays_late_aircraft.replace(-999,np.nan,inplace = True) 
df.num_of_delays_carrier.replace("1500+",1500,inplace = True) 
df.airport_name.replace("",np.nan,inplace = True) 
df.month.replace("n/a",np.nan,inplace = True) 
df.minutes_delayed_nas.replace(-999.0,np.nan,inplace = True) 
df.num_of_delays_late_aircraft.value_counts(dropna=False)
df['num_of_delays_carrier'] = df['num_of_delays_carrier'].astype(int)
df.month.replace("Febuary","February",inplace = True)
month_order = ["January", "February", "March", "April",
          "May", "June", "July", "August",
          "September", "October", "November", "December"] 
df['month'] = pd.Categorical(df['month'],categories=month_order, ordered=True)

df.head(1) 

	airport_code	airport_name	month	year	num_of_flights_total	num_of_delays_carrier	num_of_delays_late_aircraft	num_of_delays_nas	num_of_delays_security	num_of_delays_weather	num_of_delays_total	minutes_delayed_carrier	minutes_delayed_late_aircraft	minutes_delayed_nas	minutes_delayed_security	minutes_delayed_weather	minutes_delayed_total
0	ATL	Atlanta, GA: Hartsfield-Jackson Atlanta Intern...	January	2005.0	35048	1500	NaN	4598	10	448	8355	116423.0	104415	207467.0	297	36931	465533

QUESTION|TASK 2

Which airport has the worst delays? Describe the metric you chose, and why you chose it to determine the “worst” airport. Your answer should include a summary table that lists (for each airport) the total number of flights, total number of delayed flights, proportion of delayed flights, and average delay time in hours.

I used the metric of sorting the flights by airport and using a metric I found the total flights per airport as well as the total delayed flights by airports. using these I found the proportion of delay time per flight. SFO sanfrancisco has the worst proportion with .261 of the flights having delays. Then I found the average delay time and we can see that Orlando has the worst average delay time of 1.13 hours.

df2 = df.groupby("airport_code").agg(
  total_flights = ('num_of_flights_total', 'sum'),
  total_delayed_flights = ('num_of_delays_total', 'sum'),
  sum_delay_time = ('minutes_delayed_total', 'sum')
)

df2['sum_delay_time'] = df2['sum_delay_time'] / 60
df2['prop_delayed_flight'] = round(df2['total_delayed_flights'] / df2['total_flights'],3)

df2['avg_delay_time'] = round(df2['sum_delay_time'] / df2['total_delayed_flights'],2)



df2
# df2.columns

	total_flights	total_delayed_flights	sum_delay_time	prop_delayed_flight	avg_delay_time
airport_code
ATL	4430047	902443	899732.100000	0.204	1.00
DEN	2513974	468519	419556.350000	0.186	0.90
IAD	851571	168467	171391.300000	0.198	1.02
ORD	3597588	830825	939268.816667	0.231	1.13
SAN	917862	175132	137937.466667	0.191	0.79
SFO	1630945	425604	442508.216667	0.261	1.04
SLC	1403384	205160	168722.850000	0.146	0.82

QUESTION|TASK 3

What is the best month to fly if you want to avoid delays of any length? Describe the metric you chose and why you chose it to calculate your answer. Include one chart to help support your answer, with the x-axis ordered by month. (To answer this question, you will need to remove any rows that are missing the Month variable.)

I found the proportion of flight delays and total delayed flights by each month. we find that July has the most delayed flights with 319960 in the month of july.

df3 = df.groupby('month').agg(
  total_flights = ('num_of_flights_total', 'sum'),
  total_delayed_flights = ('num_of_delays_total', 'sum')
).reset_index()
df3.month.value_counts(dropna=True)

df3['total_flight_by_delay'] = round(df3['total_delayed_flights'] / df3['total_flights'],2)

df3

	month	total_flights	total_delayed_flights	total_flight_by_delay
0	January	1193018	265001	0.22
1	February	1115814	248033	0.22
2	March	1213370	250142	0.21
3	April	1259723	231408	0.18
4	May	1227795	233494	0.19
5	June	1305663	317895	0.24
6	July	1371741	319960	0.23
7	August	1335158	279699	0.21
8	September	1227208	201905	0.16
9	October	1301612	235166	0.18
10	November	1185434	197768	0.17
11	December	1180278	303133	0.26

The following graph shows us that the proportion of delays to flights showing the chances of you experiencing a delay on your flight is lowest in the month of september.

p = (ggplot(df3, aes(x='month', y='total_flight_by_delay', )) +
  geom_bar(stat = 'identity', color = 'black', fill = 'white', width = .85) +
  xlab('Month') +
  ylab('% of Flights Delayed') +
  labs(
    title="Chance of Experiencing Any Delay by Month"
  )+
  theme_classic() +
  theme(
    axis_text_y= element_text(),
    plot_title= element_text(angle=90, size=20),
    panel_background= element_rect(fill = "gray")
  )

)
p.show()

QUESTION|TASK 4

According to the BTS website, the “Weather” category only accounts for severe weather delays. Mild weather delays are not counted in the “Weather” category, but are actually included in both the “NAS” and “Late-Arriving Aircraft” categories. Your job is to create a new column that calculates the total number of flights delayed by weather (both severe and mild). You will need to replace all the missing values in the Late Aircraft variable with the mean. Show your work by printing the first 5 rows of data in a table. Use these three rules for your calculations:

a. 100% of delayed flights in the Weather category are due to weather  
a. 30% of all delayed flights in the Late-Arriving category are due to weather  
a. From April to August, 40% of delayed flights in the NAS category are due to weather. The rest of the months, the proportion rises to 65%    

The following shows the first 5 rows of airport codes. They show that the total weather delays that are not severe. we see that the orlando has the most minor weather delays.

df4 = df.copy()
df4['num_of_delays_late_aircraft'] = df4['num_of_delays_late_aircraft'].fillna(df4['num_of_delays_late_aircraft'].mean())

df4['total_weather_delay'] = df4['num_of_delays_late_aircraft'] + (df4['num_of_delays_late_aircraft'] * .3) + np.select(df4['month'].isin(["April",
          "May", "June", "July", "August"]), df4['num_of_delays_nas'] * .4, df4['num_of_delays_nas'] * .65)

df4.head()

	airport_code	airport_name	month	year	num_of_flights_total	num_of_delays_carrier	num_of_delays_late_aircraft	num_of_delays_nas	num_of_delays_security	num_of_delays_weather	num_of_delays_total	minutes_delayed_carrier	minutes_delayed_late_aircraft	minutes_delayed_nas	minutes_delayed_security	minutes_delayed_weather	minutes_delayed_total	total_weather_delay
0	ATL	Atlanta, GA: Hartsfield-Jackson Atlanta Intern...	January	2005.0	35048	1500	1109.104072	4598	10	448	8355	116423.0	104415	207467.0	297	36931	465533	2535.435294
1	DEN	Denver, CO: Denver International	January	2005.0	12687	1041	928.000000	935	11	233	3153	53537.0	70301	36817.0	363	21779	182797	2300.000000
2	IAD	NaN	January	2005.0	12381	414	1058.000000	895	4	61	2430	NaN	70919	35660.0	208	4497	134881	2469.000000
3	ORD	Chicago, IL: Chicago O'Hare International	January	2005.0	28194	1197	2255.000000	5415	5	306	9178	88691.0	160811	364382.0	151	24859	638894	4025.100000
4	SAN	San Diego, CA: San Diego International	January	2005.0	7283	572	680.000000	638	7	56	1952	27436.0	38445	21127.0	218	4326	91552	1977.600000

QUESTION|TASK 5

Using the new weather variable calculated above, create a barplot showing the proportion of all flights that are delayed by weather at each airport. Describe what you learn from this graph.

The following results show that your chances of encountering a delay whether it is from weather, security, or carrier all being factored in is the highest at IAD airport and lowest at ATL airport getting as low as 10% chance.

df4bar = df4.groupby('airport_code').agg(
  total_flights = ('num_of_flights_total', 'sum'),
  total_delayed_flights = ('total_weather_delay', 'sum'),
  num_of_delays_carrier = ("num_of_delays_carrier", 'sum'),
  num_of_delays_security = ("num_of_delays_security", 'sum'),
).reset_index()

df4bar['prop_of_delays_weather'] = round(df4bar['total_delayed_flights'] / df4bar['total_flights'],2)
df4bar['prop_of_delays_carrier'] = round(df4bar['num_of_delays_carrier'] / df4bar['total_flights'],2)
df4bar['prop_of_delays_security'] = round(df4bar['num_of_delays_security'] / df4bar['total_flights'],2)




df4bar

	airport_code	total_flights	total_delayed_flights	num_of_delays_carrier	num_of_delays_security	prop_of_delays_weather	prop_of_delays_carrier	prop_of_delays_security
0	ATL	4430047	450637.470588	173075	833	0.10	0.04	0.0
1	DEN	2513974	378356.500000	121885	985	0.15	0.05	0.0
2	IAD	851571	228643.300000	47761	272	0.27	0.06	0.0
3	ORD	3597588	495550.741176	146491	862	0.14	0.04	0.0
4	SAN	917862	236136.500000	57130	490	0.26	0.06	0.0
5	SFO	1630945	305776.200000	86417	697	0.19	0.05	0.0
6	SLC	1403384	247641.500000	65033	867	0.18	0.05	0.0

p = (ggplot(df4bar, aes(x='airport_code', y='prop_of_delays_weather', )) +
  geom_bar(stat = 'identity', color = 'black', fill = 'white', width = .85) +
  xlab('airport_code') +
  ylab('% of Flights Delayed') +
  labs(
    title="Chance of Experiencing Any Delay by Airport"
  )+
  theme_classic() +
  theme(
    axis_text_y= element_text(),
    plot_title= element_text(angle=90, size=20),
    panel_background= element_rect(fill = "gray")
  )

)
p.show()

---

STRETCH QUESTION|TASK 1

Which delay is the worst delay? Create a similar analysis as above for Weahter Delay with: Carrier Delay and Security Delay. Compare the proportion of delay for each of the three categories in a Chart and a Table. Describe your results.

The following shows the delays but all types are separated into the categories weather, carrier, and security. We can see that carrier delays are highest in IAD and SAN airports. And the highest chance of encountering a delay from weather is also IAD and SAN.

df5 = pd.melt(
  df4bar,
  id_vars= 'airport_code',
  value_vars=  ['prop_of_delays_weather', 'prop_of_delays_carrier', 'prop_of_delays_security'],
  var_name= 'delay_type', 
  value_name= 'proportion'
)

p = (
    ggplot(
        df5,
        aes(x='airport_code',
            y='proportion',
            fill='delay_type')          # <─ aesthetic mapping goes here
    )
    + geom_bar(
        stat='identity',
        colour='black',
        width=.85,
        position='dodge'               # side-by-side bars by delay type
    )
    + xlab('Airport Code')
    + ylab('Proportion of Flights Delayed')
    + ggtitle('Chance of Experiencing Any Delay by Airport')
    + theme_classic()
    + theme(
        axis_text_x=element_text(angle=45, hjust=1),  # tilt if labels overlap
        plot_title=element_text(size=20),
        panel_background=element_rect(fill="gray")
    )
)

p.show()

---